Proper well placement can improve the oil recovery and economic benefits during oilfield development. Due to the nonlinear and complex properties of well placement optimization, an effective optimization algorithm is required. In this paper, cat swarm optimization (CSO) algorithm is applied to optimize well placement for maximum net present value (NPV). CSO algorithm, a heuristic algorithm that mimics the behavior of a swarm of cats, has characteristics of flexibility, fast convergence, and high robustness. Oilfield development constraints are taken into account during well placement optimization process. Rejection method, repair method, static penalization method, dynamic penalization method and adapt penalization method are, respectively, applied to handle well placement constraints and then the optimal constraint handling method is obtained. Besides, we compare the CSO algorithm optimization performance with genetic algorithm (GA) and differential evolution (DE) algorithm. With the selected constraint handling method, CSO, GA, and DE algorithms are applied to solve well placement optimization problem for a two-dimensional (2D) conceptual model and a three-dimensional (3D) semisynthetic reservoir. Results demonstrate that CSO algorithm outperforms GA and DE algorithm. The proposed CSO algorithm can effectively solve the constrained well placement optimization problem with adapt penalization method.
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January 2019
Research-Article
Well Placement Optimization With Cat Swarm Optimization Algorithm Under Oilfield Development Constraints
Chen Hongwei,
Chen Hongwei
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
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Feng Qihong,
Feng Qihong
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fengqihong.upc@gmail.com
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fengqihong.upc@gmail.com
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Zhang Xianmin,
Zhang Xianmin
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
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Wang Sen,
Wang Sen
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
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Zhou Wensheng,
Zhou Wensheng
China National Offshore Oil Corporation
Research Institute,
Beijing 100027, China
Research Institute,
Beijing 100027, China
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Liu Fan
Liu Fan
China National Offshore Oil Corporation
Research Institute,
Beijing 100027, China
Research Institute,
Beijing 100027, China
Search for other works by this author on:
Chen Hongwei
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
Feng Qihong
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fengqihong.upc@gmail.com
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
e-mail: fengqihong.upc@gmail.com
Zhang Xianmin
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
Wang Sen
School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
China University of Petroleum (East China),
Qingdao 266580, Shandong, China
Zhou Wensheng
China National Offshore Oil Corporation
Research Institute,
Beijing 100027, China
Research Institute,
Beijing 100027, China
Liu Fan
China National Offshore Oil Corporation
Research Institute,
Beijing 100027, China
Research Institute,
Beijing 100027, China
1Corresponding author.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received February 13, 2018; final manuscript received June 21, 2018; published online July 23, 2018. Assoc. Editor: Ray (Zhenhua) Rui.
J. Energy Resour. Technol. Jan 2019, 141(1): 012902 (11 pages)
Published Online: July 23, 2018
Article history
Received:
February 13, 2018
Revised:
June 21, 2018
Citation
Hongwei, C., Qihong, F., Xianmin, Z., Sen, W., Wensheng, Z., and Fan, L. (July 23, 2018). "Well Placement Optimization With Cat Swarm Optimization Algorithm Under Oilfield Development Constraints." ASME. J. Energy Resour. Technol. January 2019; 141(1): 012902. https://doi.org/10.1115/1.4040754
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